We evaluate our method on three publicly accessible datasets including ICL-NUIM  [1]}, TUM RGB-D  [2]} and ETH3D  [3]}. ICL-NUIM is a synthetic dataset comprising 4 rendered (noise added) RGB-D sequences. For TUM RGB-D, four real captured sequences are most often compared in the literature. Both have ordinary camera moving speed (typically slower than 1m/s). For ETH3D, we are especially interested in the three challenging sequences. The three sequences, prefixed with “camera shake”, mainly contain shaking motions of RGB-D camera and exhibit increasing moving speed from camera_shake_1 to camera_shake_3. Let us refer to these three sequences as ETH3D-CS. All these datasets possess ground-truth camera trajectories.
